• How it works: Uses another language model (the “judge”) to score or critique the output of your target prompt based on specific criteria (e.g., helpfulness, clarity, adherence to instructions).
  • Best for: Subjective criteria, complex assessments, evaluating nuanced qualities like creativity or tone.

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Setup

1

Go to evaluations tab

Go to evaluations tab on a prompt in one of your projects.

2

Add evaluation

On the top right corner, click on the “Add evaluation” button.

3

Choose LLM-as-a-judge

Choose “LLM-as-a-judge” tab in the evaluation modal.

4

Choose a metric

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Metrics

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Binary

Judges whether the response meets the criteria. The resulting score is “passed” or “failed”

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Rating

Judges the response by rating it under a criteria. The resulting score is the rating

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Comparison

Judges the response by comparing the criteria to the expected output. The resulting score is the percentage of compared criteria that is met

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Custom

Judges the response under a criteria using a custom prompt. The resulting score is the value of criteria that is met

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Expected output

The expected output, also known as label, refers to the correct or ideal response that the language model should generate for a given prompt. You can create datasets with expected output columns to evaluate prompts with ground truth.

Comparison and Custom (labeled) metrics require expected output.

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Templates

We have a list of pre-configured LLM-as-a-judge templates that you can use to quickly set up evaluations. These templates cover common evaluation scenarios and can be customized to fit your specific needs.

  • Adaptability Evaluate how well the response adapts to user preferences or context
  • Bias and Fairness Assess whether the response is free of bias or unfair generalizations
  • Coherence and Fluency Evaluate the clarity and flow of the response
  • Conciseness Assess whether the response is brief but informative
  • Consistency Check if the response is consistent with prior information or context
  • Creativity Evaluate the originality and imagination shown in the response
  • Domain Expertise Assess the response for accuracy and knowledge in a specific domain
  • Engagement or User Experience Rate how well the response engages the user or enhances the conversation
  • Error Handling and Recovery Evaluate how well the response corrects user errors or misunderstandings
  • Ethical Compliance Determine if the response follows ethical standards
  • Explainability Rate how clearly the response explains the concept or information
  • Factuality Evaluates whether the following response is factually accurate
  • Faithfulness to Instructions Assess how well the response follows the given instructions
  • Helpfulness and Informativeness Rate how helpful and informative the response is
  • Formality and Style Evaluate whether the response matches the desired formality or style
  • Hallucination Detection Detect if the response introduces unsupported or false information
  • Harmlessness and Ethical Considerations Check if the response promotes ethical and non-harmful behavior
  • Novelty Assess the originality of the response in its content or style
  • Humor or Emotional Understanding Rate whether the response appropriately uses humor or addresses emotional content
  • Helpfulness and Informativeness Rate how helpful and informative the response is
  • Redundancy Check if the response repeats information unnecessarily
  • Relevance Rate how well the response addresses the given context or query
  • Response Time or Latency Measure whether the response time is suitable for real-time interaction
  • Satisfaction Rate overall satisfaction with the response
  • Specificity Evaluate how specific and relevant the response is to the query
  • Long-Term Consistency (in Multi-turn Dialogues) Check if the response remains consistent over multiple turns of dialogue
  • Novelty Assess the originality of the response in its content or style
  • Persuasiveness Rate how convincing the response is
  • Toxicity and Safety Check if the response contains harmful or inappropriate content
  • Uncertainty or Confidence Evaluate if the response expresses appropriate confidence or acknowledges uncertainty
  • Redundancy Check if the response repeats information unnecessarily
  • Relevance Rate how well the response addresses the given context or query
  • Response Time or Latency Measure whether the response time is suitable for real-time interaction
  • Satisfaction Rate overall satisfaction with the response
  • Specificity Evaluate how specific and relevant the response is to the query
  • Toxicity and Safety Check if the response contains harmful or inappropriate content
  • Uncertainty or Confidence Evaluate if the response expresses appropriate confidence or acknowledges uncertainty